Some other covid19 visualizations:

https://coronavirus.1point3acres.com/

https://coronavirus.jhu.edu/map.html

# data source https://www.census.gov/data/datasets/time-series/demo/popest/2010s-state-total.html and wikipedia
df_population <- data.frame(
  state = c("AK", "AL", "AR", "AS", "AZ", "CA", "CO", "CT", "DC", "DE", "FL", 
            "GA", "GU", "HI", "IA", "ID", "IL", "IN", "KS", "KY", "LA", "MA", 
            "MD", "ME", "MI", "MN", "MO", "MP", "MS", "MT", "NC", "ND", "NE", 
            "NH", "NJ", "NM", "NV", "NY", "OH", "OK", "OR", "PA", "PR", "RI", 
            "SC", "SD", "TN", "TX", "UT", "VA", "VI", "VT", "WA", "WI", "WV", "WY"),
  population = c(731545, 4903185, 3017804, 55465 , 7278717, 39512223, 5758736, 3565287, 705749, 973764, 21477737,
                 10617423, 165768, 1415872, 3155070, 1787065, 12671821, 6732219, 2913314, 4467673, 4648794, 6892503, 
                 6045680, 1344212,  9986857, 5639632, 6137428, 56882, 2976149, 1068778, 10488084, 762062, 1934408,
                 1359711, 8882190, 2096829, 3080156, 19453561, 11689100, 3956971, 4217737, 12801989, 3193694, 1059361,
                 5148714, 884659, 6829174, 28995881, 3205958, 8535519, 106977 , 623989, 7614893, 5822434, 1792147, 578759)
)

# The Atlantic Monthly Group (CC BY-NC 4.0)
# source: https://covidtracking.com/api

df_states <- fread("https://covidtracking.com/api/v1/states/daily.csv") %>% 
               replace(is.na(.), 0) %>%
               inner_join(df_population, by = "state")%>%
               mutate(date = as.Date(as.character(date), "%Y%m%d"))

tableau10 <- as.list(ggthemes_data[["tableau"]][["color-palettes"]][["regular"]][[1]][,2])$value
first_day <- as.Date("2020-03-15") # to select a date
today <-  as.Date(toString(max(df_states$date)))
  
kable(head(df_states, n = 3))
date state positive negative pending totalTestResults hospitalizedCurrently hospitalizedCumulative inIcuCurrently inIcuCumulative onVentilatorCurrently onVentilatorCumulative recovered dataQualityGrade lastUpdateEt dateModified checkTimeEt death hospitalized dateChecked totalTestsViral positiveTestsViral negativeTestsViral positiveCasesViral deathConfirmed deathProbable totalTestEncountersViral totalTestsPeopleViral totalTestsAntibody positiveTestsAntibody negativeTestsAntibody totalTestsPeopleAntibody positiveTestsPeopleAntibody negativeTestsPeopleAntibody totalTestsPeopleAntigen positiveTestsPeopleAntigen totalTestsAntigen positiveTestsAntigen fips positiveIncrease negativeIncrease total totalTestResultsSource totalTestResultsIncrease posNeg deathIncrease hospitalizedIncrease hash commercialScore negativeRegularScore negativeScore positiveScore score grade population
2020-09-21 AK 7838 420807 0 428645 47 0 0 0 13 0 2439 A 9/21/2020 03:59 2020-09-21T03:59:00Z 09/20 23:59 45 0 2020-09-21T03:59:00Z 428645 7089 421275 7838 45 0 0 0 0 0 0 0 0 0 0 0 0 0 2 71 1649 428645 posNeg 1720 428645 0 0 2ec1459d557a0a9f8c65f1d11f0e2ac4bdf025f3 0 0 0 0 0 0 731545
2020-09-21 AL 145780 928112 0 1059517 802 16487 0 1645 0 904 61232 A 9/21/2020 11:00 2020-09-21T11:00:00Z 09/21 07:00 2439 16487 2020-09-21T11:00:00Z 1059517 0 0 131405 2292 147 0 0 0 0 0 56961 0 0 0 0 0 0 1 818 4746 1073892 posNeg 5500 1073892 2 260 7570e181bedab66c64605b577126d7e6c27c0be7 0 0 0 0 0 0 4903185
2020-09-21 AR 76364 817238 0 891524 439 4986 228 0 95 632 66934 A+ 9/21/2020 00:00 2020-09-21T00:00:00Z 09/20 20:00 1197 4986 2020-09-21T00:00:00Z 891524 0 817238 74286 1048 149 0 0 0 0 0 0 0 0 10101 2242 21856 3300 5 641 6944 893602 posNeg 7540 893602 16 19 587b536e6faa93fe497751a7fe36885ecaffac9f 0 0 0 0 0 0 3017804

Rhode Island (as I live in RI now)

df_states %>% filter(state == "RI") %>%
    ggplot() + 
      geom_label(x = first_day, y = 650, color = "darkgray", label = "total positive", size = 2, hjust = 0) + 
      geom_text(mapping = aes(x = date, y = 600, label = positive), color = "darkgray", size = 2, angle = 90, hjust = 0)+ 
      #geom_label(x = first_day, y = 800, color = "black", label = "death", size = 2, hjust = 0) + 
      geom_label(x = first_day, y = 550, color = tableau10[2], label = "positiveIncrease", size = 2, hjust = 0) + 
      geom_label(x = first_day, y = 500, color = tableau10[1], label = "hospitalizedCurrently", size = 2, hjust = 0) + 
      # geom_line(mapping = aes(x = date, y = death), alpha = 0.7, color = "black", size = LINE_SIZE) + 
      # geom_text(mapping = aes(x = date - 0.5, y = death + 10, label = death), color = "black", size = 1.5) + 
      # geom_point(mapping = aes(x = date, y = death), color = "black", shape = 10) + 
      geom_line(mapping = aes(x = date, y = hospitalizedCurrently), alpha = 0.7, color = tableau10[1], size = LINE_SIZE) + 
      geom_text(mapping = aes(x = date - 0.5, y = hospitalizedCurrently + 10, label = hospitalizedCurrently), color =  tableau10[1], size = 1.5) + 
      geom_point(mapping = aes(x = date, y = hospitalizedCurrently), color = tableau10[1], shape = 15) + 
      geom_line(mapping = aes(x = date, y = positiveIncrease), alpha = 0.7, color = tableau10[2], size = LINE_SIZE) + 
      geom_text(mapping = aes(x = date - 0.5, y = positiveIncrease + 10, label = positiveIncrease), color =  tableau10[2], size = 1.5)+ 
      geom_point(mapping = aes(x = date, y = positiveIncrease), color = tableau10[2]) + 
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "day")) + 
      xlab("Date") + ylab("") + ggtitle("RI")

US - all states

df_states %>% group_by(date) %>%
    summarise(positiveIncrease = sum(positiveIncrease), hospitalizedCurrently = sum(hospitalizedCurrently), total = sum(positive)) %>% 
    ungroup() %>%
    ggplot() + 
     geom_label(x = first_day, y = 68000, color = "darkgray", label = "total positive: ", size = 2, hjust = 0) +
     geom_text(mapping = aes(x = date, y = 70000, label = total), color = "darkgray", size = 2, angle = 90, hjust = 0) +
     geom_label(x = first_day, y = 50000, color = tableau10[1], label = "hospitalizedCurrently", size = 2, hjust = 0) +
     geom_label(x = first_day, y = 55000, color = tableau10[2], label = "positiveIncrease", size = 2, hjust = 0) +
     geom_line(mapping = aes(x = date, y = hospitalizedCurrently), alpha = 0.7, color = tableau10[1], size = LINE_SIZE) +
     geom_text(mapping = aes(x = date - 0.5, y = hospitalizedCurrently + 1000, label = hospitalizedCurrently), color =  tableau10[1], size = 1.5) +
     geom_point(mapping = aes(x = date, y = hospitalizedCurrently), color = tableau10[1], shape = 15) +
     geom_line(mapping = aes(x = date, y = positiveIncrease), alpha = 0.7, color = tableau10[2], size = LINE_SIZE) +
     geom_text(mapping = aes(x = date - 0.5, y = positiveIncrease + 1000, label = positiveIncrease), color =  tableau10[2], size = 1.5) +
     geom_point(mapping = aes(x = date, y = positiveIncrease), color = tableau10[2]) +
     scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "day")) +
     xlab("Date") + ylab("") + ggtitle("US - positiveIncrease & hospitalizedCurrently")

US - daily top-3 contributors

If a state has been a top 3 contributor

as_top <- df_states %>%
    filter(date > first_day)%>%
    mutate(str_date = as.character(date))%>%
    group_by(str_date) %>%
    arrange(positiveIncrease, by_group = TRUE)%>%
    slice_tail(n = 3) %>%
    ungroup() %>%
    summarise(unique(state))
as_top <- unlist(as_top)
    
df_states %>%
    filter(state %in% as_top) %>%
    ggplot() +
      stat_steamgraph(mapping = aes(x = date, y = positiveIncrease, group = state, fill = state))  +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week"))  +
      scale_y_continuous(breaks = seq(-20000, 20000, by = 5000), labels = c("20000", "15000", "10000", "5000", "0", "5000", "10000", "15000", "20000")) +
      scale_fill_tableau(palette = "Tableau 20") +
      xlab("Date") + ylab("positiveIncrease") + ggtitle("If a state was a top-3 contributor")

US - positiveIncrease by state

num_lag <- 21

find_coef <- function(x, y){
  m <- lm(y ~ x)
  return(coef(m)[2])
}


df_colors <-  df_states %>%
  group_by(state)%>%
  arrange(date, .by_group = TRUE) %>%
  slice_tail(n = num_lag) %>% # last N days
  summarise(trend_coef = find_coef(date, positiveIncrease)) %>% 
  mutate(trend_color = ifelse(trend_coef > 0, "increasing", ifelse(trend_coef < 0, "decreasing", "stable"))) %>% 
  ungroup()%>%
  replace(is.na(.), 0) %>%
  select(state, trend_coef, trend_color) 
 
  
df_states %>% 
    inner_join(df_colors, by = "state") %>%
    ggplot() +
      geom_smooth(mapping = aes(x = date, y = positiveIncrease), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
      geom_line(mapping = aes(x = date, y = positiveIncrease, color = trend_color), alpha = 0.7, size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = positiveIncrease, color = trend_color), size = 1) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      scale_colour_tableau() +
      facet_wrap(state ~ ., ncol = 6, scales = "free") +
      xlab("Date") + ylab("") + ggtitle("US - positiveIncrease by state, colored by the trend of last 21 days")

df_states %>% 
    inner_join(df_colors, by = "state") %>%
    mutate(positiveIncreasePerMillion = positiveIncrease / population * 1000000)%>%
    ggplot() +
      geom_smooth(mapping = aes(x = date, y = positiveIncreasePerMillion), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
      geom_line(mapping = aes(x = date, y = positiveIncreasePerMillion, color = trend_color), alpha = 0.7, size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = positiveIncreasePerMillion, color = trend_color), size = 1) +
      scale_y_continuous(limits = c(0, 600), breaks = seq(0, 600, by = 150)) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      scale_colour_tableau() +
      facet_wrap(state ~ ., ncol = 6, scales = "free")  +
      xlab("Date") + ylab("") + ggtitle("US - positiveIncreasePerMillion by state, colored by the trend of last 21 days")

US - hospitalizedCurrently by state

df_states %>% 
    ggplot() +
      geom_smooth(mapping = aes(x = date, y = hospitalizedCurrently), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
      geom_line(mapping = aes(x = date, y = hospitalizedCurrently), alpha = 0.7, color = tableau10[3], size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = hospitalizedCurrently), color = tableau10[3], size = 1) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      facet_wrap(state ~ ., ncol = 6, scales = "free") +
      xlab("Date") + ylab("") + ggtitle("US - hospitalizedCurrently by state")

US - dailyTestPositiveRate against overallTestedPopulationRate

df_pr <- df_states %>% 
    mutate(testPositiveRate = positiveIncrease / totalTestResultsIncrease, testedPopulationRate = totalTestResults / population) %>%
    filter(testPositiveRate > 0 & testPositiveRate < 1) # rm buggy data to allow log scales
  
df_pr_colors <-  df_pr %>%
  group_by(state)%>%
  arrange(date, .by_group = TRUE) %>%
  slice_tail(n = num_lag) %>% # last N days
  summarise(trend_coef = find_coef(date, testPositiveRate)) %>% 
  mutate(trend_color = ifelse(trend_coef > 0, "increasing", ifelse(trend_coef < 0, "decreasing", "stable"))) %>% 
  ungroup()%>%
  replace(is.na(.), 0) %>%
  select(state, trend_coef, trend_color) 

df_pr %>%
 inner_join(df_pr_colors, by = "state") %>%
 ggplot() +
    geom_smooth(mapping = aes(x = testedPopulationRate, y = testPositiveRate), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
    geom_line(mapping = aes(x = testedPopulationRate, y = testPositiveRate, color = trend_color), alpha = 0.7, size = LINE_SIZE) +
    geom_point(mapping = aes(x = testedPopulationRate, y = testPositiveRate, color = trend_color), size = 1) +
    scale_x_continuous(limits = c(0, 0.60), breaks = seq(0, 0.6, by = 0.05)) +
    scale_y_continuous(limits = c(0.001, 1), trans = 'log10', breaks = c(0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5, 0.75, 1)) +
    scale_colour_tableau() +
    facet_wrap(state ~ ., ncol = 6, scales = "free")  +
    xlab("dailyTestPositiveRate") + ylab("overallTestedPopulationRate") + ggtitle("US - dailyTestPositiveRate against overallTestedPopulationRate")

US - death per 10k by state

df_states %>% 
    mutate(deathPer10K = death / population * 10000) %>%
    ggplot() +
     geom_line(mapping = aes(x = date, y = deathPer10K), alpha = 0.7, color = tableau10[3], size = LINE_SIZE) +
     geom_point(mapping = aes(x = date, y = deathPer10K), color = tableau10[3], size = 1) +
     scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
     scale_y_continuous(limits = c(0, 20), breaks = seq(0, 20, by = 5)) +
     facet_wrap(state ~ ., ncol = 6, scales = "free")  +
     xlab("date") + ylab("death per 10k") + ggtitle("US - death per 10k by state")

US - positive per 1k by state

df_states %>% 
    mutate(positivePerOneK = positive / population * 1000) %>%
    ggplot() +
      geom_line(mapping = aes(x = date, y = positivePerOneK), alpha = 0.7, color = tableau10[4], size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = positivePerOneK), color = tableau10[4], size = 1) +
      scale_y_continuous(limits = c(0, 25), breaks = seq(0, 25, by = 5)) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      facet_wrap(state ~ ., ncol = 6, scales = "free") +
      xlab("date") + ylab("") + ggtitle("US - positivePerOneK by state")

US - tested amount by state

df_states %>% 
    mutate(testResultsIncrease = positiveIncrease + negativeIncrease) %>%
    ggplot() +
      geom_smooth(mapping = aes(x = date, y = testResultsIncrease), color = "gray", alpha = 0.3, method = "loess", size = LINE_SIZE) +
      geom_line(mapping = aes(x = date, y = testResultsIncrease), alpha = 0.7, color = tableau10[7], size = LINE_SIZE) +
      geom_point(mapping = aes(x = date, y = testResultsIncrease), color = tableau10[7], size = 1) +
      scale_x_date(limits = c(first_day, today), breaks = seq(first_day, today, by = "week")) +
      facet_wrap(state ~ ., ncol = 6, scales = "free")  +
      xlab("date") + ylab("testResultsIncrease") + ggtitle("US - testResultsIncrease by state")